{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bitcoin Price Predictor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predict next day stock price using last 60 days data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import time\n",
    "from collections import deque\n",
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, CuDNNLSTM, BatchNormalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Import:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>1528968660</th>\n",
       "      <td>6489.549805</td>\n",
       "      <td>6489.560059</td>\n",
       "      <td>6489.560059</td>\n",
       "      <td>6489.549805</td>\n",
       "      <td>0.587100</td>\n",
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       "      <td>6487.370117</td>\n",
       "      <td>6487.370117</td>\n",
       "      <td>6479.410156</td>\n",
       "      <td>3.088252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1528968840</th>\n",
       "      <td>6479.410156</td>\n",
       "      <td>6479.419922</td>\n",
       "      <td>6479.419922</td>\n",
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       "      <td>6479.410156</td>\n",
       "      <td>6479.979980</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "                    low         high         open        close    volume\n",
       "time                                                                    \n",
       "1528968660  6489.549805  6489.560059  6489.560059  6489.549805  0.587100\n",
       "1528968720  6487.370117  6489.560059  6489.549805  6487.379883  7.706374\n",
       "1528968780  6479.410156  6487.370117  6487.370117  6479.410156  3.088252\n",
       "1528968840  6479.410156  6479.419922  6479.419922  6479.410156  1.404100\n",
       "1528968900  6475.930176  6479.979980  6479.410156  6479.979980  0.753000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"BTC-USD.CSV\", names=[\"time\", \"low\", \"high\",\"open\",\"close\",\"volume\"])\n",
    "df.set_index(\"time\", inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
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      "text/plain": [
       "                  close\n",
       "time                   \n",
       "1528968660  6489.549805\n",
       "1528968720  6487.379883\n",
       "1528968780  6479.410156\n",
       "1528968840  6479.410156\n",
       "1528968900  6479.979980"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[[\"close\"]]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEQ_LENGTH = 60 #no. of previous days from which to predict\n",
    "\n",
    "sc = StandardScaler()\n",
    "\n",
    "def preprocess_df(df):\n",
    "    sc_train = StandardScaler()\n",
    "    df = df.dropna()\n",
    "\n",
    "    df_array = sc.fit_transform(df)\n",
    "\n",
    "    sequential_data = []\n",
    "    prev_days = deque(maxlen=SEQ_LENGTH)\n",
    "\n",
    "    for f in df_array: #list of rows\n",
    "        prev_days.append(f)\n",
    "        if len(prev_days) == SEQ_LENGTH:\n",
    "            sequential_data.append([np.array(prev_days)[:-1], f]) # feature set and labels\n",
    "    \n",
    "    random.shuffle(sequential_data)\n",
    "\n",
    "    x = []\n",
    "    y = []\n",
    "\n",
    "    for seq, target in sequential_data:\n",
    "        x.append(seq)\n",
    "        y.append(target)\n",
    "    \n",
    "    return np.array(x), np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation and Split:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "times = sorted(df.index.values)\n",
    "\n",
    "last_5pct = times[-int(0.05*len(times))] # returns index\n",
    "\n",
    "#Splitting data\n",
    "validation_df = df[(df.index >= last_5pct)]\n",
    "df = df[(df.index < last_5pct)]\n",
    "\n",
    "train_x, train_y = preprocess_df(df)\n",
    "\n",
    "validation_x, validation_y = preprocess_df(validation_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construct RNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(CuDNNLSTM(50, input_shape=train_x.shape[1:], return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "model.add(CuDNNLSTM(50, input_shape=train_x.shape[1:]))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(1))\n",
    "\n",
    "opt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)\n",
    "\n",
    "model.compile(loss='mean_squared_error',\n",
    "\toptimizer=opt,\n",
    "\tmetrics=['mse'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 92779 samples, validate on 4827 samples\n",
      "Epoch 1/10\n",
      "92779/92779 [==============================] - 64s 689us/step - loss: 0.0782 - mean_squared_error: 0.0782 - val_loss: 0.0254 - val_mean_squared_error: 0.0254\n",
      "Epoch 2/10\n",
      "92779/92779 [==============================] - 50s 537us/step - loss: 0.0329 - mean_squared_error: 0.0329 - val_loss: 0.0194 - val_mean_squared_error: 0.0194\n",
      "Epoch 3/10\n",
      "92779/92779 [==============================] - 50s 544us/step - loss: 0.0267 - mean_squared_error: 0.0267 - val_loss: 0.0104 - val_mean_squared_error: 0.0104\n",
      "Epoch 4/10\n",
      "92779/92779 [==============================] - 51s 545us/step - loss: 0.0242 - mean_squared_error: 0.0242 - val_loss: 0.0085 - val_mean_squared_error: 0.0085\n",
      "Epoch 5/10\n",
      "92779/92779 [==============================] - 50s 539us/step - loss: 0.0222 - mean_squared_error: 0.0222 - val_loss: 0.0113 - val_mean_squared_error: 0.0113\n",
      "Epoch 6/10\n",
      "92779/92779 [==============================] - 50s 539us/step - loss: 0.0216 - mean_squared_error: 0.0216 - val_loss: 0.0085 - val_mean_squared_error: 0.0085\n",
      "Epoch 7/10\n",
      "92779/92779 [==============================] - 51s 553us/step - loss: 0.0210 - mean_squared_error: 0.0210 - val_loss: 0.0072 - val_mean_squared_error: 0.0072\n",
      "Epoch 8/10\n",
      "92779/92779 [==============================] - 51s 546us/step - loss: 0.0205 - mean_squared_error: 0.0205 - val_loss: 0.0069 - val_mean_squared_error: 0.0069\n",
      "Epoch 9/10\n",
      "92779/92779 [==============================] - 51s 555us/step - loss: 0.0201 - mean_squared_error: 0.0201 - val_loss: 0.0065 - val_mean_squared_error: 0.0065\n",
      "Epoch 10/10\n",
      "92779/92779 [==============================] - 50s 544us/step - loss: 0.0200 - mean_squared_error: 0.0200 - val_loss: 0.0079 - val_mean_squared_error: 0.0079\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1da94ee2630>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EPOCHS = 10\n",
    "BATCH_SIZE = 64\n",
    "\n",
    "model.fit(train_x, train_y,\n",
    "    epochs=EPOCHS,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    validation_data=(validation_x, validation_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(sc.inverse_transform(validation_y[-30:]), color='red', label=\"Actual Price\")\n",
    "plt.plot(sc.inverse_transform(model.predict(validation_x[-30:])), color=\"blue\", label=\"Predicted Price\")\n",
    "plt.title(\"Bitcoin Price Prediction\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
